Abstract

Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due to their nonlinear mapping nature. The ANN architecture optimization, the initial weight values of the neurons, selection of training algorithm and critical analysis and selection of the most appropriate input parameters are some important consideration for STLF. Levenberg-Marquardt (LM) algorithm for the training of the neural network is implemented in the first stage. The second stage is based on a hybrid model which combines the ANN and GA.

Keywords:

Artificial neural network, genetic algorithm, levenberg-marquardt, short term load forecast,

References

1. Adepoju, G.A., 2007. Application of neural network to load forecasting in nigerian electrical power system. Practical J. Sci. Technol., 8: 68-72.
   
2. Al-Saba, T. and E. Amin, 1999. Artificial neural networks as applied to long-term forecasting. Artif. Intell. Eng., 13(2): 189-197.
   CrossRef    
3. Chen, H., C.A. Canizares and A. Singh, 2001. ANN-based short-term load forecasting in elctricity markets. Proceeding of IEEE Power Engineering Society Winter Meeting, 2: 411-415.
   PMCid:PMC1506209    
4. Edmund, T.H.H., D. Srinivasan and A.C. Liew, 1998. Short term load forecasting using genetic algorithm and neural networks. Proceeding of EMPD International Conference on Energy Management and Power Delivery, pp: 576-581.
   
5. Eugene, D.G. and A. Feinberg, 2006. Book: Title/ Chapter: Applied Mathematics For Power Systems. Chapter 12, Load Forecasting.
   
6. Ganzalez, P.A. and J.M. Zamarreno, 2005. Prediction of hourly energy consumption in building based on a feedback artificial network. Energ. Buildings, 37: 595-601.
   CrossRef    
7. Gross, G. and F.D. Galiana, 1987. Short-term load forecasting. Proc. IEEE, 75(12): 1558-1573.
   CrossRef    
8. Hippert, H.S., C.E. Pedreira and R.C. Souza, 2001. Neural networks for short-term load forecasting: A review and evaluation. IEEE T. Power Sys., 16(1): 44-55.
   CrossRef    
9. Liu, C.X. and L. Li, 2011. Application of chaos and neural network in power load forecasting. Discrete Dyn. Nat. Soc., 2011: 12, Article ID 597634.
   CrossRef    
10. Mandal, P., T. Senjyu, N. Urasaki and T. FunabashI, 2006. A neural network based on several-hour-ahead electric load forecasting using similar days approach. Int. J. Elect. Power Energ. Syst., 28(6): 367-373.
    CrossRef    
11. Morinigo-Sotelo, D., O. Duque-Perez, L.A. Garcia-Escudero, M. Fernandez-Temprano and P. Fraile-Llorente, 2011. Short-term hourly load forecasting of a hospital using an artificial neural network. Proceeding of International Conference on Renewable Energies and Power Quality (ICREPQ'11). Las Palmas de Gran Canaria (Spain).
    CrossRef    
12. Nagi, J., K.S. Yap, S.K. Tiong and S.K. Ahmed, 2008. Electrical power load forecasting using hybrid self-organizing maps and support vector machines. Proceeding of the 2nd International Power Engineering and Optimization Conference (PEOCO 2008). Shah Alam, Selangor, Malaysia.
    PMCid:PMC4088872    
13. Nahi, K., W. René, S. Maarouf and G. Semaan, 2006. An efficient approach for short term load forecasting using artificial neural networks. Int. J. Elect. Power Energ. Syst., 28(8): 525-530.
    CrossRef    
14. Ningl, Y.,Y. Liu and Q. Ji, 2010. Bayesian-BP neural network based short-term load forecasting for power system. Proceeding of 3rd International Conference on Advanced Computer Theory and Engineering (1CACTE), pp: V2-89-V2-93.
    
15. Poplawski, T., 2008. The short-term fuzzy load prediction model. Acta Electron. Inform., 8(1): 39-43.
    
16. Rahman, S., 1990. Formulation and analysis of a rule based short term load forecasting algorithm. Proc. IEEE, 78(5): 805-816.
    CrossRef    
17. Rothe, Mrs. J.P., A.K. Wadhwani and Mrs. S. Wadhwani, 2009. Short term load forecasting using multi parameter regression. Int. J. Comput. Sci. Inform. Secur., 6(2): 303-306.
    
18. Satish, B., K.S. Swarup, S. Srinivas and A.H. Rao, 2004. Effect of temperature on short term load forecasting using an integrated ANN. Elect. Power Syst. Res., 72(1): 95-101.
    CrossRef    
19. Satpathy, H.P., 2003. Real-Coded GA for Parameter Optimization in Short-term Load Forecasting. In: Mira, J. (Ed.), IWANN 2003. Springer-Verlag, Berlin, Heidelberg, LNCS 2687, pp: 417-424.
    CrossRef    
20. Srinivasan, D., 1998. Evolving artificial neural networks for short term load forecasting. Neurocomputing, 23(1-3): 265-276.
    CrossRef    
21. Topalli, A.K., I. Erkme and I. Topalli, 2006. Intelligent short-term load forecasting in Turkey. Int. J. Elect. Power Energ. Syst., 28(7): 437-447.
    CrossRef    
22. Tzafestas, S. and E. Tzafestas, 2001. Computational intelligence techniques for short-term electric load forecasting. J. Intell. Robot. Syst., 31(1-3): 7-68.
    CrossRef    
23. Yalcinoz, T. and U. Eminoglu, 2005. Short term and medium term power distribution load forecasting by neural networks. Energ. Convers. Manage., 46(9-10): 1393-1405.
    CrossRef    
24. Yaxi, Y.X. and D. Liu, 2001. BP-GA mixed algorithms for short-term load forecasting. Proceedings of the International Conferences on Info-tech and Info-net (ICII, 2001), 4: 334-339.
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.